4 research outputs found
Cataract influence on iris recognition performance
This paper presents the experimental study revealing weaker performance of
the automatic iris recognition methods for cataract-affected eyes when compared
to healthy eyes. There is little research on the topic, mostly incorporating
scarce databases that are often deficient in images representing more than one
illness. We built our own database, acquiring 1288 eye images of 37 patients of
the Medical University of Warsaw. Those images represent several common ocular
diseases, such as cataract, along with less ordinary conditions, such as iris
pattern alterations derived from illness or eye trauma. Images were captured in
near-infrared light (used in biometrics) and for selected cases also in visible
light (used in ophthalmological diagnosis). Since cataract is a disorder that
is most populated by samples in the database, in this paper we focus solely on
this illness. To assess the extent of the performance deterioration we use
three iris recognition methodologies (commercial and academic solutions) to
calculate genuine match scores for healthy eyes and those influenced by
cataract. Results show a significant degradation in iris recognition
reliability manifesting by worsening the genuine scores in all three matchers
used in this study (12% of genuine score increase for an academic matcher, up
to 175% of genuine score increase obtained for an example commercial matcher).
This increase in genuine scores affected the final false non-match rate in two
matchers. To our best knowledge this is the only study of such kind that
employs more than one iris matcher, and analyzes the iris image segmentation as
a potential source of decreased reliability
Iris Recognition Under Biologically Troublesome Conditions - Effects of Aging, Diseases and Post-mortem Changes
This paper presents the most comprehensive analysis of iris recognition
reliability in the occurrence of various biological processes happening
naturally and pathologically in the human body, including aging, illnesses, and
post-mortem changes to date. Insightful conclusions are offered in relation to
all three of these aspects. Extensive regression analysis of the template aging
phenomenon shows that differences in pupil dilation, combined with certain
quality factors of the sample image and the progression of time itself can
significantly degrade recognition accuracy. Impactful effects can also be
observed when iris recognition is employed with eyes affected by certain eye
pathologies or (even more) with eyes of the deceased subjects. Notably,
appropriate databases are delivered to the biometric community to stimulate
further research in these utterly important areas of iris biometrics studies.
Finally, some open questions are stated to inspire further discussions and
research on these important topics. To Authors' best knowledge, this is the
only scientific study of iris recognition reliability of such a broad scope and
novelty.Comment: Accepted manuscript version of the BIOSIGNALS 2017 pape
Database of iris images acquired in the presence of ocular pathologies and assessment of iris recognition reliability for disease-affected eyes
This paper presents a database of iris images collected from disease affected
eyes and an analysis related to the influence of ocular diseases on iris
recognition reliability. For that purpose we have collected a database of iris
images acquired for 91 different eyes during routine ophthalmology visits. This
collection gathers samples for healthy eyes as well as those with various eye
pathologies, including cataract, acute glaucoma, posterior and anterior
synechiae, retinal detachment, rubeosis iridis, corneal vascularization,
corneal grafting, iris damage and atrophy and corneal ulcers, haze or
opacities. To our best knowledge this is the first database of such kind that
will be made publicly available. In the analysis the data were divided into
five groups of samples presenting similar anticipated impact on iris
recognition: 1) healthy (no impact), 2) unaffected, clear iris (although the
illness was detected), 3) geometrically distorted irides, 4) distorted iris
tissue and 5) obstructed iris tissue. Three different iris recognition methods
(MIRLIN, VeriEye and OSIRIS) were then used to find differences in average
genuine and impostor comparison scores calculated for healthy eyes and those
impacted by a disease. Specifically, we obtained significantly worse genuine
comparison scores for all iris matchers and all disease-affected eyes when
compared to a group of healthy eyes, what have a high potential of impacting
false non-match rate
Assessment of iris recognition reliability for eyes affected by ocular pathologies
This paper presents an analysis of how the iris recognition is impacted by
eye diseases and an appropriate dataset comprising 2996 iris images of 230
distinct eyes (including 184 illness-affected eyes representing more than 20
different eye conditions). The images were collected in near infrared and
visible light during a routine ophthalmological practice. The experimental
study shows four valuable results. First, the enrollment process is highly
sensitive to those eye conditions that make the iris obstructed or introduce
geometrical distortions. Second, even those conditions that do not produce
visible changes to the iris structure may increase the dissimilarity among
samples of the same eyes. Third, eye conditions affecting iris geometry, its
tissue structure or producing obstructions significantly decrease the iris
recognition reliability. Fourth, for eyes afflicted by a disease, the most
prominent effect of the disease on iris recognition is to cause segmentation
errors. To our knowledge this is the first database of iris images for
disease-affected eyes made publicly available to researchers, and the most
comprehensive study of what we can expect when the iris recognition is deployed
for non-healthy eyes.Comment: Manuscript accepted for publication at IEEE BTAS 2015. arXiv admin
note: text overlap with arXiv:1809.0016